The invention relates generally to the field of non-invasive imaging and more specifically to the field of limited angle tomographic imaging, and the field of tomographic imaging with few projections. In particular, the present invention relates to anatomy based reconstruction techniques for use in limited angle tomographic imaging and tomographic imaging with few projections.
Digital tomosynthesis is a three-dimensional X-ray imaging technique, where typically few projection radiographs are acquired for varying positions of an X-ray source relative to the imaged object. The detector is positioned generally opposite of the X-ray source and may be stationary in certain embodiments. From the plurality of projection images, data representative of 3D structures within the imaged object can be created using a suitable reconstruction algorithm. However, reconstruction of the imaged object is difficult due to incomplete information (i.e., the absence of densely spaced projections over the full angular range).
A basic shift-and-add algorithm and/or a simple backprojection algorithm are direct reconstruction methods used to generate tomographic images that exhibit a relatively poor image quality, with low contrast and a significant artifact level. Other advanced direct reconstruction techniques, as well as iterative reconstruction techniques, may be employed with the goal of improving image quality. For example, ART (algebraic reconstruction technique), which is an iterative update of the simple backprojection reconstruction that enforces the reprojection consistency constraint is one such technique. With ART, a re-projection of the final reconstructed 3D dataset for one of the considered projection angles is substantially identical to the true projection image at that angle. ART and similar iterative methods include approaches where a first reconstruction is obtained using a direct reconstruction method (generally using simple backprojection), which is then improved in subsequent steps by iteratively updating the reconstructed three-dimensional dataset using information about the difference between original projection image and reprojected three-dimensional dataset. However, the current tomosynthesis reconstruction techniques are not very effective in addressing problems such as efficient separation of overlying tissue, enhancement of the contrast, particularly of small structures, and minimization of artifacts.
It is therefore desirable to provide an improved reconstruction technique that efficiently separates the overlying tissue, provides better contrast and minimizes artifacts.